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Model selection is the task of selecting a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection. Given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice (Occam's razor).

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rdf:type	agent yago:WikicatStatisticalModels yago:Assistant109815790 yago:CausalAgent100007347 yago:LivingThing100004258 yago:Model110324560 yago:Object100002684 yago:Organism100004475 yago:Person100007846 yago:PhysicalEntity100001930 yago:Worker109632518 yago:YagoLegalActor yago:YagoLegalActorGeo yago:Whole100003553
rdfs:label	Model selection (en) 모형 선택 (ko) Выбор статистической модели (ru) 模型选择 (zh) Обирання моделі (uk)
rdfs:comment	모형 선택은 주어진 데이터에서 일련의 후보 모델에서 통계적 모델을 선택하는 작업이다. 가장 간단한 경우, 기존의 데이터 집합을 고려한다. 그러나 수집된 데이터가 모델 선택 문제에 잘 적합하도록 실험 설계도 이 작업에 포함될 수 있다. 예측력이나 설명력이 유사한 후보 모델을 감안할 때 가장 단순한 모델은 최선의 선택일 가능성이 높다. 모델 선택은 불확실성 하에서 의사 결정이나 최적화를 목적으로 대규모 연산 모델 집합에서 몇 가지 대표적인 모델을 선택하는 문제도 언급할 수 있다. (ko) 模型选择（英語：Model selection）是在给定数据的情况下，在一组候选模型中选定最优模型的过程。在最简单的情形之下，给定数据可以是已存在的数据。不过，在复杂的情形下，模型选择也可能牵涉到实验设计，以便能够来进行模型选择。诸多候选模型的预测或解释能力相近，但根据奥卡姆剃刀原则，最简单的模型往往是最好的选择，这有助于避免过拟合。在时，或是在不确定条件下进行优化时，模型选择也可以指代从大量计算模型中选取少数代表性模型的问题。 (zh) Model selection is the task of selecting a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection. Given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice (Occam's razor). (en) Выбор модели — это задача выбора статистической модели из набора моделей-кандидатов по имеющимся данным. В простейшем случае рассматривается существующий набор данных. Однако задача может вовлекать планирование экспериментов, так что сбор данных связан с задачей выбора модели. Если заданы кандидаты в модели с одинаковой силой предсказания или объяснения, наиболее простая модель скорее всего будет лучшим выбором (бритва Оккама). (ru) Обира́ння моде́лі (англ. model selection) — задача обирання статистичної моделі з множини моделей-кандидатів на заданих даних. В найпростіших випадках розглядають вже наявний набір даних. Проте до цієї задачі входить і планування таких експериментів, щоби збирані дані добре підходили для задачі обирання моделі. За заданих моделей-кандидатів подібної передбачувальної або пояснювальної сили найкращим вибором, швидше за все, буде найпростіша модель. (uk)
foaf:depiction
dcterms:subject	Model selection Regression variable selection Mathematical and quantitative methods (economics)
Wikipage page ID	3664073 (xsd:integer)
Wikipage revision ID	1123403261 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayes factor Bayesian information criterion Scientific method Scientific modelling Bias of an estimator Variance Decision theory Design of experiments Deviance information criterion Institute of Mathematical Statistics Watanabe–Akaike information criterion Analysis of competing hypotheses Estimator Galileo Consistency (statistics) Cross-validation (statistics) Statistical inference Optimal design Annual Review of Statistics and Its Application Likelihood-ratio test Machine learning Bias-variance dilemma Statistical model Structural risk minimization Feature selection Journal of Applied Ecology Brill Publishers CRC Press Data collection Log-linear analysis Stein's example Akaike information criterion All models are wrong Model selection Regression variable selection Curve fitting Data transformation (statistics) Parameter identification problem False discovery rate Focused information criterion Goodness of fit Hannan–Quinn information criterion Journal of Multivariate Analysis PRESS statistic Stepwise regression Statistical model specification Statistical parameters Mathematical and quantitative methods (economics) Chapman & Hall Chi-squared test Ecology (journal) Efficiency (statistics) Identifiability Analysis Automated machine learning Polynomial Freedman's paradox I.i.d. IEEE Transactions on Signal Processing Kluwer Academic Publishers Minimum description length Minimum message length Mallows's Cp Science (journal) Statistical model validation Exploratory data analysis Kashyap information criterion Occam's razor Robust statistics Statistical Science Occam’s Razor

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